Mapping Polyclonal HIV-1 Antibody Responses via Next-Generation Neutralization Fingerprinting

Area of Science:

• Immunology
• Virology
• Computational Biology

Background:

• Computational neutralization fingerprinting (NFP) is an established method for predicting antibody epitope specificities in HIV-1 infection.
• Accurate characterization of polyclonal antibody responses is crucial for understanding HIV-1 pathogenesis and vaccine development.

Purpose of the Study:

• To develop and validate next-generation NFP algorithms for improved prediction accuracy at individual and cohort levels.
• To enhance the analysis of epitope specificities in polyclonal antibody responses to HIV-1.

Main Methods:

• Development of algorithms for optimized neutralization panel selection, prediction confidence estimation, and identification of novel epitope specificities.
• Application of next-generation NFP to computationally simulated and actual HIV-1 infected donor samples.
• Large-scale serologic analysis of approximately 150 broadly neutralizing HIV-1 infected donor samples.

Main Results:

• Next-generation NFP algorithms significantly improved the detection of multiple epitope specificities in simulated and real HIV-1 samples, doubling detection in simulated sera.
• The new algorithms successfully identified both VRC01-like and PG9-like antibody specificities in a single donor sample.
• Cohort-level analysis revealed a potential link between HIV-1 clade and elicited epitope specificities, with an enrichment of 10E8-like antibodies in clade B infections.

Conclusions:

• Next-generation NFP algorithms represent a substantial advancement in analyzing polyclonal antibody responses to HIV-1.
• These improved NFP tools facilitate individual and cohort-level serologic analysis, aiding in the design of epitope-specific HIV-1 vaccines.
• The findings support the utility of NFP in guiding vaccine strategies based on regional HIV-1 clade prevalence.